Source code for langchain.vectorstores.chroma
"""Wrapper around ChromaDB embeddings platform."""
from __future__ import annotations
import logging
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.vectorstores.base import VectorStore
if TYPE_CHECKING:
    import chromadb
    import chromadb.config
logger = logging.getLogger()
def _results_to_docs(results: Any) -> List[Document]:
    return [doc for doc, _ in _results_to_docs_and_scores(results)]
def _results_to_docs_and_scores(results: Any) -> List[Tuple[Document, float]]:
    return [
        # TODO: Chroma can do batch querying,
        # we shouldn't hard code to the 1st result
        (Document(page_content=result[0], metadata=result[1] or {}), result[2])
        for result in zip(
            results["documents"][0],
            results["metadatas"][0],
            results["distances"][0],
        )
    ]
[docs]class Chroma(VectorStore):
    """Wrapper around ChromaDB embeddings platform.
    To use, you should have the ``chromadb`` python package installed.
    Example:
        .. code-block:: python
                from langchain.vectorstores import Chroma
                from langchain.embeddings.openai import OpenAIEmbeddings
                embeddings = OpenAIEmbeddings()
                vectorstore = Chroma("langchain_store", embeddings.embed_query)
    """
    _LANGCHAIN_DEFAULT_COLLECTION_NAME = "langchain"
    def __init__(
        self,
        collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,